Data de-duplication

ABSTRACT

A method, executed by a computer, for de-duplicating data includes receiving a dataset, pivoting the dataset along a set of columns that have a common domain to provide a pivoted dataset, de-duplicating the pivoted dataset to provide a de-duplicated dataset, and using the de-duplicated dataset. De-duplicating the pivoted dataset may include computing similarity scores for records that have different primary keys and merging records that have a similarity score that exceeds a selected threshold value. The method may include determining the set of columns having a common domain by referencing a business catalog and/or conducting a data classification operation on some or all of the columns of the dataset. The method may also include pivoting the dataset along another set of columns that have a different common domain. A computer system and computer program product corresponding to the method are also disclosed herein.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of data processing,and more particularly to data de-duplication.

Data de-duplication is an important operation when cleansing a datasource. Data de-duplication is typically achieved by searching in adatabase for duplicated records that represent the same entity andmerging records so that a single record remains for the entity. Insearching for duplicate records, de-duplication techniques typicallydistribute the records into multiple groups in a way that similarrecords may fall into the same group, conduct a column by columncomparison of two selected records within the same group, and compute amatch score indicating the probability or likelihood that the selectedrecords represent the same entity. Record pairs that have a sufficientlyhigh score are considered duplicates and may be merged to create asingle ‘golden’ record.

SUMMARY

As disclosed herein, a method, executed by a computer, forde-duplicating data includes receiving a dataset, pivoting the datasetalong a set of columns that have a common domain to provide a pivoteddataset, de-duplicating the pivoted dataset to provide a de-duplicateddataset, and using the de-duplicated dataset. De-duplicating the pivoteddataset may include computing similarity scores for records that havedifferent primary keys and merging records with a similarity score thatexceeds a selected value. The method may include determining the set ofcolumns having a common domain by referencing a business catalog and/orconducting a data classification operation on some or all of the columnsof the dataset. The method may also include pivoting the datasetadditional sets of columns that have additional common domains. Themethod provides improved data de-duplication. A computer system andcomputer program product corresponding to the method are also disclosedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of one embodiment of a dataprocessing environment in which at least some of the embodimentsdisclosed herein may be deployed;

FIG. 2 is a flowchart depicting one embodiment of a dataset processingmethod;

FIG. 3 is a flowchart depicting one embodiment of a recordde-duplication method;

FIGS. 4a to 4e are text diagrams depicting an example dataset at variousstages of processing; and

FIG. 5 is a block diagram depicting one example of a computing apparatus(i.e., computer) suitable for executing the methods disclosed herein.

DETAILED DESCRIPTION

Applicants have observed that currently available data de-duplicationtechniques work well as long as each column in a dataset has a uniquedomain which is the same for all of the records. However, currentlyavailable techniques do not respond well to having similar informationin multiple columns of the compared records. For example, some datasources may have multiple fields for email addresses and phone numbers.Unless the placement of the data in those fields is the same in everyinstance, currently available techniques are unable to consistentlyidentify and de-duplicate records that represent the same entity.

The embodiments disclosed herein were developed in response to theobservations of the Applicants and improve the de-duplication ofdatasets that have multiple fields with a common domain.

FIG. 1 is a functional block diagram of one embodiment of a dataprocessing environment 100. As depicted, the data processing environment100 includes a data processor 110, one or more data sources 120 (e.g.,data sources 120 a, 120 b, and 120 c), a network 130, and one or moredata clients 140 (e.g., data clients 140 a and 140 b). The dataprocessing environment 100 is one example of an environment in which atleast some of the embodiments disclosed herein may be deployed.

The data processor 110 processes datasets provided by, or retrievedfrom, the data sources 120. The data sources 120 may be accessible tothe data processor 110 via the network 130. One or more data clients 140may also be connected to the data processor 110 via the network 130. Insome embodiments the data sources 120 are also data clients 140.

Data 122, provided by the data sources 120, may be disparate data thatdoes not facilitate direct comparisons. For example, the data recordsthat comprise the data 122 may not have the same number of columns ormay have different or unknown data in one or more columns. Some of thedata 122 may have one or more business catalogs (i.e., metadata)associated therewith (not shown in the drawings) that generally describethe content of each column of data, while other data 122 may not havebusiness catalogs associated therewith.

In contrast to the data sources 120, the data clients 140 have a needfor consistent data that is readily comparable. As will be describedhereinafter, and despite the foregoing issues, the data processor 110 isconfigured to compare and process the data 122 and provide consistentdata 142.

It should be noted that the data processor 110 may include internal andexternal hardware components, as depicted and described in furtherdetail with respect to FIG. 5. Furthermore, the network 130 can be anycombination of connections and protocols that will supportcommunications between the data processor 110, the data sources 120, andthe data clients (i.e., data consumers) 140. For example, the network130 can be a local area network (LAN), a wide area network (WAN) such asthe Internet, or a combination of the two, and can include wired,wireless, or fiber optic connections.

FIG. 2 is a flowchart depicting one embodiment of a dataset processingmethod 200. As depicted, the dataset processing method 200 includesreceiving (210) a dataset, receiving (220) common domain information,pivoting (230) the dataset, de-duplicating (240) the pivoted dataset,and using (250) the de-duplicated dataset. The dataset processing method200 may be used (e.g., by the data processor 110) to process and improvedatasets that potentially have similar information in multiples columns,as is commonly found in datasets collected from disparate sources.

Receiving (210) a dataset may include receiving data records from one ormore sources. Some of the fields of the dataset may have missing entries(i.e., null values). In certain embodiments, data from disparate sourcesis merged into a record format that is a superset of the disparatedataset formats. Receiving (210) the dataset may also includedetermining if the dataset does not include a primary key field (i.e.,column) and auto-generating a primary key value for each record.

Receiving (220) common domain information may include receivinginformation specifying which columns in the dataset (e.g., a supersetfrom disparate sources) have a common domain. The common domaininformation may include one or more domain types and a listing of thecolumns (i.e., a set of columns) that conform to each type. In someembodiments, the common domain information is extracted from a businesscatalog or other source of pre-identified column domain information. Inother embodiments, the domains for the columns are identified by dataprocessing techniques. For example, the domains for data columns may bedetermined according to the embodiments disclosed in commonly assignedU.S. Pat. No. 8,666,998 entitled “Handling data sets,” which isincorporated herein by reference. Alternately, columns of data may becompared for similarity of their names and/or formatting of their datain order to determine common domains. For example, phone numbers withindistinct columns may have common prefixes and/or similar formatting thatcould be identified via a similarity test and/or a data parsingoperation, or the columns containing those phone numbers may havesimilar names (e.g., that differ with only a suffix), indicating thatthe columns contain information of same domain.

Pivoting (230) the dataset may include conducting pivoting operations oneach set of columns with a common domain. The pivoting operations mayresult in the conversion of a set of data columns within each recordthat have a common domain to a set of data rows (i.e., multiple datarecords) that have a single column for the common domain. For example,pivoting operations conducted on a data record with a set of n columnsthat have a common domain may result in a set of n records (i.e., rows)with a single column for the common domain. In some embodiments,multiple pivoting operations corresponding to multiple sets of columnswith a common domain (for each set) are conducted. Multiple pivotingoperations may result in many pivoted data records being generated fromeach record in the dataset. See FIGS. 4a to 4d for a specific examplethat shows two pivoting operations for each data record.

De-duplicating (240) the pivoted dataset may include discardingduplicate records or merging records that have duplicated information toprovide a de-duplicated dataset. Discarding duplicate records or mergingrecords that have duplicated information may result in multiple pivotedrecords being replaced by a single data record. In some embodiments,de-duplication is achieved by an un-pivoting operation for records witha common primary key and discarding duplicate records that have adifferent primary key. Consequently, pivoted records generated frommultiple records in the original dataset may be replaced with a singledata record resulting in de-duplication that is responsive to havingsimilar data within different columns.

Using (250) the de-duplicated dataset may include additional processingincluding conventional data processing operations (e.g., map/reduceoperations, ETL (Extract Transform Load) operations, MDM (Master DataManagement) operations, reporting operations, and the like).

One of skill in the art will appreciate the simplicity of the method200. In one embodiment, before actual de-duplication of the records isconducted, columns having the same data class/domain are detected usingeither information stored in a business catalog (e.g., attached businessterms for instance) or using a data classification algorithm (e.g., asdisclosed in commonly assigned U.S. Pat. No. 8,666,998 entitled“Handling data sets,” which is incorporated herein by reference). Ifmultiple columns belonging to the same domain are found, a primary keyis generated for each row (if needed), the columns of each domain arepivoted in one single column/multiple rows, and a de-duplication processis conducted on the transformed dataset.

FIG. 3 is a flowchart depicting one embodiment of a recordde-duplication method 300. As depicted, the record de-duplication method300 includes retrieving (310) a pair of data records, computing (320) asimilarity for the record pair, determining (330) whether the recordsare sufficiently similar, determining (340) whether the records have thesame primary key value, determining (350) whether a previous similarityscore exists for other records having the same pair of primary keyvalues, determining (360) whether the new similarity score is greater,saving (370) the similarity score, determining (380) whether additionalrecords need to be processed, and merging (390) records with sufficientsimilarity. The record de-duplication method 300 is one example of thede-duplicating operation 240 shown in FIG. 2 and generally describedabove.

Retrieving (310) a pair of data records may include reading a new datarecord (e.g., row) from a dataset and maintaining a record that waspreviously retrieved. Initially, a pair of data records may be retrievedin preparation of the comparing operation 320.

Computing (320) a similarity for the record pair may include computing ascore based on the similarity of individual columns. For example, thesimilarity score can be computed by assigning a weight to each columnthat increases the score when two records have the same value for thesame column and/or by assigning a cost to each column that decreases thescore when two records have different values for the same column. Insome embodiments, advanced comparison functions like the Levenshteindistance (edit-distance) are calculated instead of doing a strict valuecomparison, in order to at least partially increase the score if thevalues for a column are nearly the same but not exactly the same.Columns that are the result of pivoting operations may, or may not be,factored into the similarity score.

Determining (330) whether the records are sufficiently similar mayinclude comparing a similarity score generated by the comparingoperation 320 with a selected threshold value. In some embodiments, theselected threshold value is determined by analyzing a histogram ofsimilarity scores. For example, in one embodiment a similarity valuethat is between a median similarity score and a maximum similarity scoremay be used as the selected threshold value. In another embodiment, atesting dataset with pre-identified duplicates is used to select athreshold value for the similarity score.

Determining (340) whether the records have the same primary key valuemay include checking whether the primary key values are identical. Ifthe primary keys are identical, the method ignores that the records arepotential duplicates and skips to determining (380) whether additionalrecords need to be processed. If the primary keys are different, themethod continues by determining (350) whether a previous similarityscore exists.

Determining (350) whether a previous similarity score exists may includeaccessing a table of similarity scores for pairs of primary keys anddetermining whether an entry exists for the primary key paircorresponding to the compared records. If a previous similarity scoredoes not exist, the method advances to determining (360) whether the newsimilarity score is greater than the previous similarity score. If thenew similarity score is greater, the method continues by saving (370)the similarity score and determining (380) whether additional recordsneed to be processed.

Determining (380) whether additional records need to be processed mayinclude testing a current row index against one or more values or someother procedure well known to those of skill in the art. If additionalrecords need to be processed, the depicted method 300 loops to theretrieving operation 310 and continues processing. Otherwise the methodadvances to the merging operation 390.

Merging (390) records with sufficient similarity may include combiningrecords to include all of the information available in the duplicaterecords which (in some instances) may result in more columns than theoriginal received dataset. In one embodiment, records are merged byconducting a de-pivoting operation on a pivoted column and the resultingcolumns are identical to those in the original received dataset.Subsequent to merging, the method terminates or is suspended untiladditional records need to be processed.

FIGS. 4a to 4e are text diagrams depicting an example of a sampledataset at various stages of processing. FIG. 4a shows a sample receiveddataset consisting of 5 records with a name column, two email addresscolumns, three phone number columns, and a zip-code (postal code)column. As depicted, some of the fields may be empty.

FIG. 4b shows that a primary key field may be auto-generated andincluded in each record. Subsequently, due to a pivoting operation onthe two email columns, FIG. 4c shows that some of the records arereplicated and provided with different values for a single email columnresulting in 8 records rather than 5. Similarly, due to a pivotingoperation on the three email columns, FIG. 4d shows that many of therecords are replicated and provided with different values for a singlephone column resulting in 18 records rather than 8.

Although it may appear that the pivoting operations simply increase theamount of data, FIG. 4e shows that duplicates are eliminated by themethods disclosed herein resulting in just two records. Depending on howthe de-duplication operations (e.g., operations 350 and 360) areconducted, the resulting records may or may not have the same columns asthe original received dataset. For example, the records shown in FIG. 4ehave a primary key field and a second zip-code field that were notpresent in the original dataset.

FIG. 5 is a block diagram depicting components of a computer 500suitable for executing the methods disclosed herein. The computer 500may be one embodiment of the data processor 110 depicted in FIG. 1. Itshould be appreciated that FIG. 5 provides only an illustration of oneembodiment and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

As depicted, the computer 500 includes communications fabric 502, whichprovides communications between computer processor(s) 505, memory 506,persistent storage 508, communications unit 512, and input/output (I/O)interface(s) 515. Communications fabric 502 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric502 can be implemented with one or more buses.

Memory 506 and persistent storage 508 are computer readable storagemedia. In the depicted embodiment, memory 506 includes random accessmemory (RAM) 516 and cache memory 518. In general, memory 506 caninclude any suitable volatile or non-volatile computer readable storagemedia.

One or more programs may be stored in persistent storage 508 forexecution by one or more of the respective computer processors 505 viaone or more memories of memory 506. The persistent storage 508 may be amagnetic hard disk drive, a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 508 may also be removable. Forexample, a removable hard drive may be used for persistent storage 508.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage508.

Communications unit 512, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 512 includes one or more network interface cards.Communications unit 512 may provide communications through the use ofeither or both physical and wireless communications links.

I/O interface(s) 515 allows for input and output of data with otherdevices that may be connected to computer 500. For example, I/Ointerface 515 may provide a connection to external devices 520 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 520 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards.

Software and data used to practice embodiments of the present inventioncan be stored on such portable computer readable storage media and canbe loaded onto persistent storage 508 via I/O interface(s) 515. I/Ointerface(s) 515 also connect to a display 522. Display 522 provides amechanism to display data to a user and may be, for example, a computermonitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The embodiments disclosed herein include a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry out themethods disclosed herein.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method, executed by a computer, forde-duplicating data, the method comprising: receiving a dataset in theform of a database data structure organized with rows respectivelycorresponding to records and columns respectively corresponding to fieldvalues for the records; receiving common domain information for thedataset, wherein the common domain information defines a set of columnshaving a common domain; in response to determining that the dataset doesnot have a primary key field, generating a primary key for each row inthe dataset; pivoting the dataset along the set of columns having acommon domain to provide a pivoted dataset having multiple rows for theset of columns and a single column for the common domain; computingsimilarity scores for records of the dataset, wherein the similarityscores are based on similar data existing within different columns ofrecords of the dataset; determining a median similarity score and amaximum similarity score of the similarity scores; determining athreshold value that is between the median similarity score and themaximum similarity score; and merging records that have a similarityscore that exceeds the threshold value to obtain a de-duplicateddatabase data structure having no duplicate records, wherein the recordsare merged by de-pivoting the dataset for rows having a common primarykey and discarding duplicate rows that have a different primary key. 2.The method of claim 1, wherein computing similarity scores for recordsof the dataset comprises computing similarity scores for records thathave different primary keys.
 3. The method of claim 1, furthercomprising determining the set of columns having a common domain.
 4. Themethod of claim 3, wherein determining the set of columns having acommon domain comprises conducting a data classification operation on aplurality of columns of the dataset.
 5. The method of claim 3, whereindetermining the set of having a common domain comprises referencing abusiness catalog.
 6. The method of claim 1, further comprising pivotingthe dataset along another set of columns having a common domain.